CVML-Pose: Convolutional VAE based Multi-Level Network for Object 3D Pose Estimation

Abstract

Most vision-based 3D pose estimation approaches typically rely on knowledge of object’s 3D model, depth measurements, and often require time-consuming iterative refinement to improve accuracy. However, these can be seen as limiting factors for broader real-life applications. The main motivation for this paper is to address these limitations. To solve this, a novel Convolutional Variational Auto-Encoder based Multi-Level Network for object 3D pose estimation (CVML-Pose) method is proposed. Unlike most other methods, the proposed CVML-Pose implicitly learns an object’s 3D pose from only RGB images encoded in its latent space without knowing the object’s 3D model, depth information, or performing a post-refinement. CVML-Pose consists of two main modules: (i) CVML-AE representing convolutional variational autoencoder, whose role is to extract features from RGB images, (ii) Multi-Layer Perceptron and K-Nearest Neighbor regressors mapping the latent variables to object 3D pose including, respectively, rotation and translation. The proposed CVML-Pose has been evaluated on the LineMod and LineMod-Occlusion benchmark datasets. It has been shown to outperform other methods based on latent representations and achieves comparable results to the state-of-the-art, but without use of a 3D model or depth measurements. Utilizing the t-Distributed Stochastic Neighbor Embedding algorithm, the CVML-Pose latent space is shown to successfully represent objects’ category and topology. This opens up a prospect of integrated estimation of pose and other attributes (possibly also including surface finish or shape variations), which, with real-time processing due to the absence of iterative refinement, can facilitate various robotic applications.